Medical imaging is an umbrella term encompassing a number of imaging techniques including Magnetic Resonance Imaging (MRI), X-ray imaging, Computed Tomography (CT), and Ultrasound imaging (US), and is used primarily as supporting tool for diagnosis and monitoring of diseases. From a computational perspective, the community has engaged in a wide variety of tasks concerning the automated interpretation of medical imaging and enabling a range of applications. Machine learning (ML) shows significant successes for applications like automated localization and delineation of lesions and anatomies [1]}, [2]} as well as for the automated alignment of scans between patients and mapping of patient anatomy into canonical interpretation spaces [3]}, [4]}, [5]}, [6]}.
Despite good in silico results, many of the approaches fail to translate into the clinical practice. While the reasons behind this can be complex and diverse, many have as a common factor the inability to adapt and be robust in clinical practice. Mapping this to the popular systems engineering framework of Technology Readiness Levels (TRL) [7]} as shown in fig:mltrl, medical imaging AI/ML applications often skip from TRL 4 – proof of concept– to TRL 7 –deployment– overlooking the very important TRLs 5 and 6 that make new systems robust to real world conditions. In fig: our mltrl we exhibit a finer grain view on the steps between TRL 6 and 7 that we deem to be important during the development of production-level medical imaging ML algorithms.
<FIGURE><FIGURE>